Noise reduction by fuzzy image filtering

Ville, Dimitri Van De; Nachtegael, Mike; Weken, Dietrich Van der; Kerre, Etienne; Philips, Wilfried; Lemahieu, Ignace

doi:10.1109/tfuzz.2003.814830

Cited by 234 publications

(74 citation statements)

References 13 publications

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“…3 we have illustrated the mean execution time of both methods (for a neighbourhood size of 5 × 5 and 9 × 9 (i.e. K = 2 and K = 4, respectively)) and the fuzzy non-wavelet based GOA filter [26]. We observe that the non-wavelet based method GOA performs much faster than the two wavelet based algorithms.…”

Section: Resultsmentioning

confidence: 93%

“…-FUZZY: the GOA filter [26], FRINRM [27] (fuzzy randomly valued impulse noise reduction method), HAF [28] (histogram adaptive fuzzy), EIFCF [29] (extended iterative fuzzy control based filter), SFCF [29] (smoothing fuzzy control based filter), DWMAV [30] (decreasing weight fuzzy filter with moving average centre), GMAV [30] (Gaussian fuzzy filter with moving average centre), AFSF [31] (the adaptive fuzzy switching filter), FSB [32,33] (fuzzy similarity filter) and AWFM [34,35] (adaptive weighted fuzzy mean).…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

A New Fuzzy-Based Wavelet Shrinkage Image Denoising Technique

Schulte

Huysmans

Pižurica

et al. 2006

Advanced Concepts for Intelligent Vision Systems

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Abstract. This paper focuses on fuzzy image denoising techniques. In particular, we investigate the usage of fuzzy set theory in the domain of image enhancement using wavelet thresholding. We propose a simple but efficient new fuzzy wavelet shrinkage method, which can be seen as a fuzzy variant of a recently published probabilistic shrinkage method [1] for reducing adaptive Gaussian noise from digital greyscale images. Experimental results show that the proposed method can efficiently and rapidly remove additive Gaussian noise from digital greyscale images. Numerical and visual observations show that the performance of the proposed method outperforms current fuzzy non-wavelet methods and is comparable with some recent but more complex wavelets methods. We also illustrate the main differences between this version and the probabilistic version and show the main improvements in comparison to it.

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Section: Resultsmentioning

confidence: 93%

Section: Resultsmentioning

confidence: 99%

A New Fuzzy-Based Wavelet Shrinkage Image Denoising Technique

Schulte

Huysmans

Pižurica

et al. 2006

Advanced Concepts for Intelligent Vision Systems

View full text Add to dashboard Cite

show abstract

“…The implementation of our fuzzy Filter [31,32,33,34] in the image restoration process was discussed. All the techniques and operations provide an efficient working.…”

Section: Resultsmentioning

confidence: 99%

“…It does not distinguish local variation due to noise and due to image structure proposed filter can clean an image completely of noise without making it blurry (Although that can produce an image that is very plasticy if depth level of the Tool is increased. ).It presents a new technique for filtering narrow-tailed and medium narrow-tailed noise by a fuzzy filter [31,32,34]. In the future work, this proposed fuzzy filter will be focused on improving the system performance for both gray scale and color images.…”

Section: Resultsmentioning

confidence: 99%

A Comparative Study of Gaussian Noise Removal Methodologies for Gray Scale Images

Tulin¹

2017

IJCA

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Image filtering is a technique to preserve important signal elements such as edges, smoothing the details of the image to make images appear clear and sharpener. Among all the non linear concepts to suppress Gaussian noise the fuzzy logic based approaches are important as they are capable of reasoning with vague and uncertain information. In this study, have made comparative study with the existing noise reduction methods where the images contaminated with Gaussian noise and found the best result by using fuzzy image filter with the help of fuzzy rules which make use of membership functions. In this article, to perform fuzzy smoothing, fuzzy derivative concept is also applied. This method provides better input for further image processing techniques. It also increases the contrast of the images, fine details and sharpening the edges as well. This comparative study, is made by numerical measures and visual inspection.

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“…To get a better result, based on the pixels in the neighborhood diverse aggregations are defined. In [46,47,48,49] authors have proposed various neighborhood based rule-sets for fuzzy image processing. Along with this we can also use the Median Aggregation, Average aggregation, Iterative averaging aggregation, Absolute Maximum.…”

Section: Methodsmentioning

confidence: 99%

Implementation of Fuzzy Thresholding for Segmentation of Images

Kumari¹,

Enireddy²

2017

IJCA

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Segmentation is a process of dividing an image into multiple regions making it easier for the analysis. It is also an important step in Image processing. It also has number of applications which includes Medical field to analyze a disease, scientific fields including, engineering and technology, face recognition and object. Many algorithms and techniques have been developed for the image segmentation. To remove distinctive areas from a picture an immediate and straight forward method is using thresholding. It looks for a worldwide esteem that boosts the partition between yield classes. Noisy or uneven illuminated images pose a challenge for segmentation. The utilization of solitary hard limit esteem is absolutely the wellspring of imperative division mistakes in numerous situations like boisterous images or uneven illumination. In this paper a multiregion thresholding technique is displayed to overcome the normal downsides of thresholding strategies when images are debased with artifacts and commotion. Pixels from the images are related to various yield centroids by means of fuzzy membership function, evading any underlying hard choice. To make this strategy robust to noise and artifacts it utilizes spatial data through a local aggregation step where the participation level of every pixel is adjusted by neighborhood data that considers the enrollments of the encompassing pixels. The results are then compared with the existing techniques and results obtained are satisfactory.

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Noise reduction by fuzzy image filtering

Cited by 234 publications

References 13 publications

A New Fuzzy-Based Wavelet Shrinkage Image Denoising Technique

A New Fuzzy-Based Wavelet Shrinkage Image Denoising Technique

A Comparative Study of Gaussian Noise Removal Methodologies for Gray Scale Images

Implementation of Fuzzy Thresholding for Segmentation of Images

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